Human Resource Analytics on Data Science Employment Based on Specialized Skill Sets with Salary Prediction
Main Article Content
The research aims to perform meaningful human resource analysis on data science employment using the strong influences of specialized skills set with assisting salary prediction. With explosive big data development, a data science job shortage has occurred with high accurate recruitment demand to hire suitable professionals for specific data science roles. To achieve such outcomes, the current data science employment trends were analyzed based on a secondary dataset. Useful analytics insights for job securement and better career development were provided through the main dashboard. Besides, the significant in-demand data science skill variables were also identified for further effective model building. Particularly, certain data pre-processing techniques were performed extensively to prepare and optimize the dataset for the mentioned human resource analytics purposes. The ensemble model was selected as the most suitable salary prediction model with the lowest Average Squared Error (ASE) on validation. Despite the low prediction accuracy caused by numerous filtered skill variables, the salary prediction model’s main objective was to interpret the relationships between input variables and the target salary levels variable. Overall, the results from both the human resource analytic dashboard and salary prediction model were tally where a detailed analytic report was provided to encourage different data science roles with specific and effective career development guidance, using salary as the motivation key.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
One, T., 2020. Malaysia is ready for Industry 4.0. [Online], Available: https://www.tmone.com.my/resources/think-tank/article/malaysia-is-ready-for-industry-4-0/.
V.Sindhu, Anitha, G. & Geetha, R., 2021. Industry 4.0-A Breakthrough in artificial Intelligence the Internet of Things and Big Data towards the next digital revolution for high business outcome and delivery. Journal of Physics: Conference Series, Volume 1937, pp. 1-7.
Jain, K., 2019. Big job opportunities in data science & machine learning. Express Computer, pp. 1-3.
DuBois, J., 2020. The Data Scientist Shortage in 2020. [Online], Available: https://quanthub.com/data-scientist-shortage-2020/.
More, A., Naik, A. & Rathod, S., 2021. PREDICT-NATION Skills Based Salary Prediction for Freshers. SSRN Electronic Journal.
Olavsrud, T., 2020. What is a data analyst? A key role for data-driven business decisions. [Online], Available: https://www.cio.com/article/217583/what-is-a-data-analyst-a-key-role-for-data-driven-business-decisions.html#:~:text=Data%20analysts%20work%20with%20data,predict%2C%20and%20improve%20business%20performance.
Metwalli, S. A., 2020. 10 Different Data Science Job Titles and What They Mean. [Online], Available: https://towardsdatascience.com/10-different-data-science-job-titles-and-what-they-mean-d385fc3c58ae.
Shin, T., 2021. The Most In-Demand Skills for Data Scientists in 2021. [Online], Available: https://towardsdatascience.com/the-most-in-demand-skills-for-data-scientists-in-2021-4b2a808f4005.
Vulpen, E. v., 2021. aihr. [Online], Available: https://www.aihr.com/blog/what-is-hr-analytics/.
Valamis, 2021. Human Resource (HR) Analytics. [Online], Available: https://www.valamis.com/hub/hr-analytics
Nagpal, T. & Mishra, M., 2021. Analyzing Human Resource Practices For Decision Making in Banking Sector using HR analytics. Materials Today: Proceedings.
Ameer, M., Rahul, S. P. & Manne, D., 2020. Human Resource Analytics using Power Bi Visualization Tool. Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2020), pp. 1184-1189.
Das, S., Barik, R. & Mukherjee, A., 2020. Salary Prediction using Regression Techniques. International Conference on Industry Interative Innovations in Science and Engineering, pp. 1-5.
Lothe, P. D. M. et al., 2021. Salary Prediction using Machine Learning. International Journal of Advance Sciencetic Research and Engineering Trends, 6(5), pp. 199-202.
Pawha, A. & Kamthania, D., 2019. Quantitative analysis of historical data for prediction of job salary in India - A case study. Journal of Statistics & Management Systems, 22(2), pp. 187-198.
Dutta, S., Halder, A. & Dasgupta, K., 2018. Design of a novel Prediction Engine for predicting suitable salary for a job. 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 275-279.
Zhang, J. & Cheng, J., 2019. Study of Employment Salary Forecast using KNN Algorithm. International Conference on Modeling, Simulation and Big Data Analysis, pp. 166-170.
Kavlakoglu, E., 2020. AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the Difference?. [Online]
Sun, Y. et al., 2021. Market-oriented job skill valuation with cooperative composition neural network. Nature Communications, Volume 12, pp. 1-11.
Zhu, H., 2021. Research on Human Resource Recommendation Algorithm Based on Machine Learning. Hindawi Scientific Programming, pp. 1-10.
Wang, Z., Sugaya, S. & Nguyen, D. P., 2019. Salary Prediction using Bidirectional-GRU-CNN Model. 2019 The Association for Natural Language Processing, pp. 292-295.
Chen, L., Sun, Y. & Thakuriah, P., 2020. Modelling and Predicting Individual Salaries in United Kingdom with Graph Convolutional Network. Hybrid Intelligent Systems 2018. Advances in Intelligent Systems and Computing, pp. 61-74.
Hotz, N., 2022. What is CRISP DM?. [Online], Available: https://www.datascience-pm.com/crisp-dm-2/
Lamott, K., 2022. San Francisco. [Online], Available: https://www.britannica.com/place/San-Francisco-California
tusimple, 2022. about us. [Online], Available: https://www.tusimple.com/